New video conferencing encoding standards such as H.264 employ Context based Adaptive Binary Arithmetic Decoding (CABAC). In CABAC data is encoded based upon the relationship between the most probable next data and other data. The most probable data is encoded in fewer bits than other data. Many types of image data can be transmitted in this form. This application discloses an example of encoding of a significance map, but other data types are feasible.
Image data compression often employs a spatial to frequency transform of blocks of image data known as macroblocks. A Discrete Cosine Transform (DCT) is typically used for this spatial to frequency transform. Most images have more information in the low frequency bands than in the high frequency bands. It is typical to arrange and encode such data in frequency order from low frequency to high frequency. Generally such an arrangement of data will produce a highest frequency with significant data that is lower than the highest possible encoded frequency. This permits the data for frequencies higher than the highest frequency with significant data to be coded via an end-of-block code. Such an end-of-block code implies all remaining higher frequency data is insignificant. This technique saves coding the bits that might have been devoted to the higher frequency data.
The H.264 video conferencing standard uses significance map to perform run-level information encoding after quantization. Every coefficient that is non-significant (zero) is encoded as 0. If a coefficient is significant, that is non-zero, and it is not the last such significant coefficient in the block, then it is encoded as 10. If the coefficient is the last significant coefficient in the block, then it is encoded as 11. If the coefficient is significant and is also the last possible coefficient in the block, then it is encoded as 10. Such a coefficient would be known as the last coefficient in the block by a count of the block coefficients.
A straight forward manner of CABAC decoding such data employs a series of conditional branches. Such conditional branching code is not well matched to a pipelined data processor which experiences a pipeline hit upon each conditional branch. Each taken conditional branch requires that later instructions already partially executed within the pipeline be aborted and new instructions be processed within the pipeline. This serves to place a limit on processing speed because data processors tend to be more deeply pipelined at higher operating frequencies. Software loop unrolling may reduce this problem. In any event, conventional CABAC decoding is not well matched to exploiting instruction level parallelism of a very long instruction word (VLIW) data processor such as the Texas Instruments TMS320C6000 series.